Rec-CFSVD++: Implementing Recommendation System Using Collaborative Filtering and Singular Value Decomposition (SVD)++

2021 ◽  
pp. 1-19
Author(s):  
Taushif Anwar ◽  
V. Uma ◽  
Gautam Srivastava
Keyword(s):  
Singular Value Decomposition ◽  
Collaborative Filtering ◽  
Error Rate ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Matrix Decomposition ◽  
Cold Start ◽  
Singular Value ◽  
Decomposition Approach ◽  
Value Decomposition

In recommender systems, Collaborative Filtering (CF) plays an essential role in promoting recommendation services. The conventional CF approach has limitations, namely data sparsity and cold-start. The matrix decomposition approach is demonstrated to be one of the effective approaches used in developing recommendation systems. This paper presents a new approach that uses CF and Singular Value Decomposition (SVD)[Formula: see text] for implementing a recommendation system. Therefore, this work is an attempt to extend the existing recommendation systems by (i) finding similarity between user and item from rating matrices using cosine similarity; (ii) predicting missing ratings using a matrix decomposition approach, and (iii) recommending top-N user-preferred items. The recommender system’s performance is evaluated considering Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Performance evaluation is accomplished by comparing the systems developed using CF in combination with six different algorithms, namely SVD, SVD[Formula: see text], Co-Clustering, KNNBasic, KNNBaseline, and KNNWithMeans. We have experimented using MovieLens 100[Formula: see text]K, MovieLens 1[Formula: see text]M, and BookCrossing datasets. The results prove that the proposed approach gives a lesser error rate when cross-validation ([Formula: see text]) is performed. The experimental results show that the lowest error rate is achieved with MovieLens 100[Formula: see text]K dataset ([Formula: see text], [Formula: see text]). The proposed approach also alleviates the sparsity and cold-start problems and recommends the relevant items.

scholarly journals A Hybrid Deep Collaborative Filtering Approach for Recommender Systems

2021 ◽  
Author(s):  
Kirubahari R ◽  
Miruna Joe Amali S
Keyword(s):  
Singular Value Decomposition ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Cold Start ◽  
Singular Value ◽  
User Preferences ◽  
Boltzmann Machine ◽  
Latent Features ◽  
Value Decomposition ◽  
Cold Start Problem

Abstract Recommender Systems (RS) help the users by showing better products and relevant items efficiently based on their likings and historical interactions with other users and items. Collaborative filtering is one of the most powerful technique of recommender system and provides personalized recommendation for users by prediction rating approach. Many Recommender Systems generally model only based on user implicit feedback, though it is too challenging to build RS. Conventional Collaborative Filtering (CF) techniques such as matrix decomposition, which is a linear combination of user rating for an item with latent features of user preferences, but have limited learning capacity. Additionally, it has been suffering from data sparsity and cold start problem due to insufficient data. In order to overcome these problems, an integration of conventional collaborative filtering with deep neural networks is proposed. A Weighted Parallel Deep Hybrid Collaborative Filtering based on Singular Value Decomposition (SVD) and Restricted Boltzmann Machine (RBM) is proposed for significant improvement. In this approach a user-item relationship matrix with explicit ratings is constructed. The user - item matrix is integrated to Singular Value Decomposition (SVD) that decomposes the matrix into the best lower rank approximation of the original matrix. Secondly the user-item matrix is embedded into deep neural network model called Restricted Boltzmann Machine (RBM) for learning latent features of user- item matrix to predict user preferences. Thus, the Weighted Parallel Deep Hybrid RS uses additional attributes of user - item matrix to alleviate the cold start problem. The proposed method is verified using two different movie lens datasets namely, MovieLens 100K and MovieLens of 1M and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The results indicate better prediction compared to other techniques in terms of accuracy.

scholarly journals Recommendation system using the k-nearest neighbors and singular value decomposition algorithms

2021 ◽  
Vol 11 (6) ◽  
pp. 5541
Author(s):  
Badr Hssina ◽  
Abdelkader Grota ◽  
Mohammed Erritali
Keyword(s):  
Singular Value Decomposition ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Hybrid Approach ◽  
Singular Value ◽  
User Preferences ◽  
Decomposition Algorithms ◽  
K Nearest Neighbors ◽  
The Matrix ◽  
Value Decomposition

<span>Nowadays, recommendation systems are used successfully to provide items (example: movies, music, books, news, images) tailored to user preferences. Amongst the approaches existing to recommend adequate content, we use the collaborative filtering approach of finding the information that satisfies the user by using the reviews of other users. These reviews are stored in matrices that their sizes increase exponentially to predict whether an item is relevant or not. The evaluation shows that these systems provide unsatisfactory recommendations because of what we call the cold start factor. Our objective is to apply a hybrid approach to improve the quality of our recommendation system. The benefit of this approach is the fact that it does not require a new algorithm for calculating the predictions. We are going to apply two algorithms: k-nearest neighbours (KNN) and the matrix factorization algorithm of collaborative filtering which are based on the method of (singular-value-decomposition). Our combined model has a very high precision and the experiments show that our method can achieve better results.</span>

scholarly journals Distributed Singular Value Decomposition Method for Fast Data Processing in Recommendation Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2284
Author(s):  
Krzysztof Przystupa ◽  
Mykola Beshley ◽  
Olena Hordiichuk-Bublivska ◽  
Marian Kyryk ◽  
Halyna Beshley ◽  
...  
Keyword(s):  
Distributed Systems ◽  
Singular Value Decomposition ◽  
Data Processing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Recommendation Systems ◽  
Singular Value ◽  
Singular Value Decomposition Method ◽  
Value Decomposition ◽  
Svd Method

The problem of analyzing a big amount of user data to determine their preferences and, based on these data, to provide recommendations on new products is important. Depending on the correctness and timeliness of the recommendations, significant profits or losses can be obtained. The task of analyzing data on users of services of companies is carried out in special recommendation systems. However, with a large number of users, the data for processing become very big, which causes complexity in the work of recommendation systems. For efficient data analysis in commercial systems, the Singular Value Decomposition (SVD) method can perform intelligent analysis of information. With a large amount of processed information we proposed to use distributed systems. This approach allows reducing time of data processing and recommendations to users. For the experimental study, we implemented the distributed SVD method using Message Passing Interface, Hadoop and Spark technologies and obtained the results of reducing the time of data processing when using distributed systems compared to non-distributed ones.

scholarly journals A singular value decomposition approach for improved taxonomic classification of biological sequences

BMC Genomics ◽  
2011 ◽  
Vol 12 (Suppl 4) ◽  
pp. S11 ◽  
Author(s):  
Anderson R Santos ◽  
Marcos A Santos ◽  
Jan Baumbach ◽  
John A McCulloch ◽  
Guilherme C Oliveira ◽  
...  
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Taxonomic Classification ◽  
Biological Sequences ◽  
Decomposition Approach ◽  
Value Decomposition

scholarly journals Applying uncertainty reduction strategy for improving performance of questionnaire technique of solving cold user problem

2021 ◽  
Vol 13 (2) ◽  
pp. 47-53
Author(s):  
M. Abubakar ◽  
K. Umar
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Research Work ◽  
Recommendation Systems ◽  
Cold Start ◽  
New Product ◽  
Uncertainty Reduction ◽  
Experimental Result ◽  
Reduction Strategy ◽  
Product Recommendation

Product recommendation systems are information filtering systems that uses ratings and predictions to make new product suggestions. There are many product recommendation system techniques in existence, these include collaborative filtering, content based filtering, knowledge based filtering, utility based filtering and demographic based filtering. Collaborative filtering techniques is known to be the most popular product recommendation system technique. It utilizes user’s previous product ratings to make new product suggestions. However collaborative filtering have some weaknesses, which include cold start, grey sheep issue, synonyms issue. However the major weakness of collaborative filtering approaches is cold user problem. Cold user problem is the failure of product recommendation systems to make product suggestions for new users. Literature investigation had shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon). This research work addresses the weakness of personalized questionnaire technique by applying uncertainty reduction strategy to improve the result obtained from administering personalized questionnaire. In our experimental design we perform four different experiments; Personalized questionnaire approach of solving user based coldstart was implemented using Movielens dataset of 1M size, Personalized questionnaire approach of solving user based cold start was implemented using Movielens dataset of 10M size, Personalized questionnaire with uncertainty reduction was implemented using Movielens dataset of 1M size, and also Personalized  questionnaire with uncertainty reduction was implemented using Movielens dataset of 10M size. The experimental result shows RMSE, Precision and Recall improvement of 0.21, 0.17 and 0.18 respectively in 1M dataset and 0.17, 0.14 and 0.20 in 10M dataset respectively over personalized questionnaire.

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